20,158 research outputs found
View Selection in Semantic Web Databases
We consider the setting of a Semantic Web database, containing both explicit
data encoded in RDF triples, and implicit data, implied by the RDF semantics.
Based on a query workload, we address the problem of selecting a set of views
to be materialized in the database, minimizing a combination of query
processing, view storage, and view maintenance costs. Starting from an existing
relational view selection method, we devise new algorithms for recommending
view sets, and show that they scale significantly beyond the existing
relational ones when adapted to the RDF context. To account for implicit
triples in query answers, we propose a novel RDF query reformulation algorithm
and an innovative way of incorporating it into view selection in order to avoid
a combinatorial explosion in the complexity of the selection process. The
interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201
LINVIEW: Incremental View Maintenance for Complex Analytical Queries
Many analytics tasks and machine learning problems can be naturally expressed
by iterative linear algebra programs. In this paper, we study the incremental
view maintenance problem for such complex analytical queries. We develop a
framework, called LINVIEW, for capturing deltas of linear algebra programs and
understanding their computational cost. Linear algebra operations tend to cause
an avalanche effect where even very local changes to the input matrices spread
out and infect all of the intermediate results and the final view, causing
incremental view maintenance to lose its performance benefit over
re-evaluation. We develop techniques based on matrix factorizations to contain
such epidemics of change. As a consequence, our techniques make incremental
view maintenance of linear algebra practical and usually substantially cheaper
than re-evaluation. We show, both analytically and experimentally, the
usefulness of these techniques when applied to standard analytics tasks. Our
evaluation demonstrates the efficiency of LINVIEW in generating parallel
incremental programs that outperform re-evaluation techniques by more than an
order of magnitude.Comment: 14 pages, SIGMO
Incremental View Maintenance For Collection Programming
In the context of incremental view maintenance (IVM), delta query derivation
is an essential technique for speeding up the processing of large, dynamic
datasets. The goal is to generate delta queries that, given a small change in
the input, can update the materialized view more efficiently than via
recomputation. In this work we propose the first solution for the efficient
incrementalization of positive nested relational calculus (NRC+) on bags (with
integer multiplicities). More precisely, we model the cost of NRC+ operators
and classify queries as efficiently incrementalizable if their delta has a
strictly lower cost than full re-evaluation. Then, we identify IncNRC+; a large
fragment of NRC+ that is efficiently incrementalizable and we provide a
semantics-preserving translation that takes any NRC+ query to a collection of
IncNRC+ queries. Furthermore, we prove that incremental maintenance for NRC+ is
within the complexity class NC0 and we showcase how recursive IVM, a technique
that has provided significant speedups over traditional IVM in the case of flat
queries [25], can also be applied to IncNRC+.Comment: 24 pages (12 pages plus appendix
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